Merge branch '26-cpi-more-fine-grained-control-of-predict-methods' of…

… github.com:mind-inria/hidimstat into 26-cpi-more-fine-grained-control-of-predict-methods

examples/plot_variable_importance_classif.py

@@ -224,9 +224,9 @@
 # important, with a p-value lower than 0.05, using a diamond marker. We also highlight
 # the true important features, used to generate the target variable, with a star marker.
 # While the linear model captures the importance of the age, it fails to capture the the
-# importance of the weight and height because of it's lack of expressivity. Using a
+# importance of the weight and height because of its lack of expressivity. Using a
 # polynomial kernel, the non-linear model captures the importance of the weight and height.
-# Finally, the CPI method controld for false positive discoveries contrarily to the PI method
+# Finally, the CPI method controls for false positive discoveries contrarily to the PI method
 # which identifies spurious important features simply because of the correlation structure of
 # the features.